The generalization capabilities, for learned subtle image pattern categories, of neural network and algorithmic classification techniques are described. Several neural network and algorithmic techniques have been applied to a set of feature vectors extracted from thermal infrared images which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders, particularly with soft tissue injuries such as whiplash in which there are few widely agreed upon diagnostic standards. The results of this research indicate that the backpropagation neural network produces the best classification results and provides significantly better generalization from a set of training patterns. Results of unsupervised classification of the data using clustering algorithms and the Adaptive Resonance Theory (ART2) neural network demonstrate the difficulties of learning and of generalization of patterns from such data.>

A report is presented on a comparison between neural network and algorithmic classification techniques applied to a specific thermography program: the analysis of image patterns which characterize the extent of whiplash injury. Thermography recently has been reported to have clinical utility in a multitude of neuromusculoskeletal disorders. Of particular import is the application of thermography to soft-tissue injuries in which there are few diagnostic gold standards. Likewise, neural networks have proven to be powerful pattern separators and classifiers in a variety of real-world problems. This is primarily due to their capabilities of pattern completion and nonlinear separability in feature space. Research results show that backpropagation neural networks can accurately classify up to 90% of the whiplash thermal images, while conventional algorithmic classifiers accurately classify only up to 75%. Slight differences were found in the classification accuracy of two commercially available backpropagation implementations

The authors report on the application of neurocomputing techniques to a specific thermography program: the analysis of image patterns which characterize the extent of whiplash injury. A specific sequence of preprocessing steps designed to optimize neurocomputer classification of biomedical image patterns with symmetry is described. Data acquisition methods, preprocessing steps, and experimental results are discussed. The results presented are derived from incorporating neurocomputing analysis as part of an overall image characterization process. It is demonstrated that the features do represent certain characteristics of the pathology present in the images.>

A family of neural networks and learning algorithms is introduced: the plastic self-adaptive learning machines (PSALM), together with a new interpretation of these neural networks as hyperpolyhedra in the N-dimensional Euclidean space. These networks self-adapt to a continually changing environment by properly changing the orientation of the faces of a hyperpolyhedron as well as its volume. The current structure of the hyperpolyhedron reflects the structure of the current outside world. The network optimally classifies its noise-distorted excitations into categories, after a competition between all possible categories. New categories can be created, and the old ones can be changed, or be forgotten if they are not used for a long time.>

A class of large-scale decision systems is described, and a multilevel, multi-intelligence information/knowledge/control structure is defined. The description is in a form that features the various levels of signal processing, data fusion, autonomous fuzzy logic and pattern recognition (via neural networks), system knowledge base, and personal expert system, and the way in which these processors are integrated so as to form an operational model for the overall decision system.>